The second category is a set of dialog manager (DM) modules. Similar to DM in task bots, it consists of a dialog state tracker, which keeps track of the current dialog state (e.g., by storing and encoding search interactions, including current and past user requests and system responses, in working memory), and a dialog policy, which selects action modules based on the dialog state.
Since the dialog policy only picks action classes by activating the corresponding action modules (e.g., topic shifting) but allows the selected action module to generate primary actions (e.g., what the next topic to shift), the CIR system uses a hierarchical policy: (1) a high-level policy to select action modules, and (2) a set of low-level policies, one for each action module, to generate system responses.
In addition, we might include in DM a personalization module to tailor retrieval results to an individual's interests by incorporating information about the individual (e.g., from her user profile) beyond the specific query provided. Although personalization recently becomes a controversial topic due to the concerns about privacy and effectiveness
It was reported that Google steps back on personalization because they found it did not really help improve search results and had a risk of cause users lose trust in Google due to data privacy. See e.g., https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html,
the topic needs to be revisited in the context of CIR because a dialog agent is more describable to be personal [1]}, [2]}, [3]}. One challenge is around Responsible AI, which needs to be considered in developing all the CIR modules and data files, as illustrated in Figure REF  (Bottom) to defend against harms and mitigate potential toxicity and bias.
